Un modello linguistico locale, privato 100%, sul tuo smartphone!!
Summary
Un modello linguistico locale e privato (Qwen 3 da 1.5B e 4B quantizzati) può girare offline su smartphone, con fine-tuning e LoRA distillato da un 32B.
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Un modello 100% locale sul tuo smartphone!
A developer shares their success in fine-tuning Qwen 3 models (1.5B and 4B) for local use on smartphones, with a downloadable APK that works offline, and plans for a Windows version.
Un modello 100% locale, anche sul tuo smarphone!
Rilasciata un'interfaccia per gestire due piccoli modelli (4B e 1.7B) che girano localmente sullo smartphone. Il 4B funziona bene su telefoni di fascia alta; il 1.7B ha problemi di stabilità con il reasoning, in fase di miglioramento con fine-tuning approfondito usando 130k esempi e distillazione da un teacher 32B.
@AdinaYakup: MiniCPM V4.6 a 1B MLLM that actually runs on your phone, just released by @OpenBMB 1B - Apache2.0 Runs on iOS, Android,…
OpenBMB has released MiniCPM V4.6, a 1B-parameter multimodal large language model optimized for mobile devices under the Apache 2.0 license. It features mixed visual token compression and claims approximately 1.5x faster throughput than Qwen3.5 0.8B while running natively on iOS, Android, and HarmonyOS.
Got local Qwen 3.5/3.6 generating meeting summaries entirely offline on an M4 Max. Demo with Wi-Fi off. This is the future.
The Hedy meeting app now supports fully offline AI summaries using local models like Qwen and Gemma via llama.cpp, with options for bring-your-own-model and hardware-aware model selection. The update enables Wi-Fi-free operation on Apple Silicon and Windows GPUs, though cloud still offers higher speed and quality.
LLiMba: Sardinian on a Single GPU -- Adapting a 3B Language Model to a Vanishing Romance Language
The article introduces LLiMba, a 3B parameter model adapted from Qwen2.5 for Sardinian using continued pretraining and supervised fine-tuning on a single consumer GPU. It evaluates various LoRA configurations, finding that adapter capacity significantly impacts performance and factual accuracy in low-resource language adaptation.